---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- wild
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: P.E.R.S_WILD
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wild
      type: wild
      config: WildReceipt
      split: test
      args: WildReceipt
    metrics:
    - name: Precision
      type: precision
      value: 0.8621359223300971
    - name: Recall
      type: recall
      value: 0.8556090846524432
    - name: F1
      type: f1
      value: 0.8588601036269431
    - name: Accuracy
      type: accuracy
      value: 0.9165934548649243
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# P.E.R.S_WILD

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wild dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3319
- Precision: 0.8621
- Recall: 0.8556
- F1: 0.8589
- Accuracy: 0.9166

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 4000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.0789 | 100  | 1.7570          | 0.4014    | 0.0807 | 0.1343 | 0.5202   |
| No log        | 0.1579 | 200  | 1.2158          | 0.5444    | 0.3515 | 0.4272 | 0.6895   |
| No log        | 0.2368 | 300  | 0.9862          | 0.6676    | 0.4763 | 0.5559 | 0.7534   |
| No log        | 0.3157 | 400  | 0.8539          | 0.6740    | 0.5898 | 0.6291 | 0.7883   |
| 1.3192        | 0.3946 | 500  | 0.7150          | 0.7489    | 0.6325 | 0.6858 | 0.8208   |
| 1.3192        | 0.4736 | 600  | 0.6731          | 0.7487    | 0.6716 | 0.7080 | 0.8288   |
| 1.3192        | 0.5525 | 700  | 0.6345          | 0.7588    | 0.6848 | 0.7199 | 0.8357   |
| 1.3192        | 0.6314 | 800  | 0.5903          | 0.7671    | 0.7181 | 0.7418 | 0.8472   |
| 1.3192        | 0.7103 | 900  | 0.5273          | 0.7743    | 0.7718 | 0.7731 | 0.8690   |
| 0.7013        | 0.7893 | 1000 | 0.4923          | 0.7939    | 0.7555 | 0.7742 | 0.8689   |
| 0.7013        | 0.8682 | 1100 | 0.4811          | 0.8147    | 0.7619 | 0.7874 | 0.8742   |
| 0.7013        | 0.9471 | 1200 | 0.4694          | 0.8006    | 0.7985 | 0.7995 | 0.8812   |
| 0.7013        | 1.0260 | 1300 | 0.4429          | 0.8246    | 0.8058 | 0.8151 | 0.8866   |
| 0.7013        | 1.1050 | 1400 | 0.4302          | 0.8135    | 0.8051 | 0.8093 | 0.8863   |
| 0.4844        | 1.1839 | 1500 | 0.4364          | 0.7964    | 0.8245 | 0.8102 | 0.8875   |
| 0.4844        | 1.2628 | 1600 | 0.4445          | 0.8012    | 0.8299 | 0.8153 | 0.8857   |
| 0.4844        | 1.3418 | 1700 | 0.4021          | 0.8175    | 0.8244 | 0.8209 | 0.8918   |
| 0.4844        | 1.4207 | 1800 | 0.3886          | 0.8290    | 0.8193 | 0.8241 | 0.8958   |
| 0.4844        | 1.4996 | 1900 | 0.3708          | 0.8271    | 0.8372 | 0.8321 | 0.9000   |
| 0.411         | 1.5785 | 2000 | 0.3910          | 0.8356    | 0.8310 | 0.8333 | 0.8996   |
| 0.411         | 1.6575 | 2100 | 0.3550          | 0.8419    | 0.8399 | 0.8409 | 0.9069   |
| 0.411         | 1.7364 | 2200 | 0.3499          | 0.8374    | 0.8451 | 0.8413 | 0.9066   |
| 0.411         | 1.8153 | 2300 | 0.3532          | 0.8301    | 0.8512 | 0.8405 | 0.9050   |
| 0.411         | 1.8942 | 2400 | 0.3763          | 0.8285    | 0.8471 | 0.8377 | 0.9018   |
| 0.3641        | 1.9732 | 2500 | 0.3508          | 0.8529    | 0.8410 | 0.8469 | 0.9067   |
| 0.3641        | 2.0521 | 2600 | 0.3616          | 0.8507    | 0.8384 | 0.8445 | 0.9083   |
| 0.3641        | 2.1310 | 2700 | 0.3705          | 0.8485    | 0.8511 | 0.8498 | 0.9086   |
| 0.3641        | 2.2099 | 2800 | 0.3527          | 0.8436    | 0.8562 | 0.8498 | 0.9118   |
| 0.3641        | 2.2889 | 2900 | 0.3383          | 0.8658    | 0.8475 | 0.8566 | 0.9135   |
| 0.2824        | 2.3678 | 3000 | 0.3395          | 0.8527    | 0.8523 | 0.8525 | 0.9124   |
| 0.2824        | 2.4467 | 3100 | 0.3364          | 0.8622    | 0.8478 | 0.8549 | 0.9140   |
| 0.2824        | 2.5257 | 3200 | 0.3383          | 0.8431    | 0.8619 | 0.8524 | 0.9125   |
| 0.2824        | 2.6046 | 3300 | 0.3377          | 0.8530    | 0.8586 | 0.8558 | 0.9132   |
| 0.2824        | 2.6835 | 3400 | 0.3389          | 0.8481    | 0.8629 | 0.8554 | 0.9135   |
| 0.2928        | 2.7624 | 3500 | 0.3319          | 0.8621    | 0.8556 | 0.8589 | 0.9166   |
| 0.2928        | 2.8414 | 3600 | 0.3341          | 0.8555    | 0.8575 | 0.8565 | 0.9153   |
| 0.2928        | 2.9203 | 3700 | 0.3341          | 0.8536    | 0.8603 | 0.8569 | 0.9153   |
| 0.2928        | 2.9992 | 3800 | 0.3305          | 0.8556    | 0.8636 | 0.8596 | 0.9167   |
| 0.2928        | 3.0781 | 3900 | 0.3313          | 0.8579    | 0.8613 | 0.8596 | 0.9166   |
| 0.2487        | 3.1571 | 4000 | 0.3326          | 0.8550    | 0.8604 | 0.8577 | 0.9160   |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1